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Texas discontinued state-sponsored business restrictions and mask mandates on March 10, 2021, and mandated that no government officials, including public school officials, may implement mask requirements even in areas where COVID-19 hospitalizations comprised more than 15% of hospitalizations. Nonetheless, some public school districts began the 2021–2022 school year with mask mandates in place. We used quasi-experimental methods to analyze the impact of school mask mandates, which appear to have resulted in approximately 40 fewer student cases per week in the first eight weeks of school. (Am J Public Health. 2022;112(6):871–875. https://doi.org/10.2105/AJPH.2022.306769 )

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Racial and ethnic inequities in survival persist for children with acute lymphoblastic leukemia (ALL). In the US, there are strong associations between SES, race/ethnicity, and place of residence. This is evidenced by ethnic enclaves: neighborhoods with high concentrations of ethnic residents, immigrants, and language isolation. The Latinx enclave index (LEI) can be used to investigate how residence in a Latinx enclave is associated with health outcomes. We studied the association between LEI score and minimal residual disease (MRD) in 142 pediatric ALL patients treated at Texas Children’s Hospital. LEI score was associated with end-induction MRD positivity (OR per unit increase 1.63, CI 1.12–2.46). There was also a significant trend toward increased odds of MRD positivity among children living in areas with the highest enclave index scores. MRD positivity at end of induction is associated with higher incidence of relapse and lower overall survival among children with ALL; future studies are needed to elucidate the exact causes of these findings and to improve ALL outcomes among children residing within Latinx enclaves. Supplemental data for this article is available online at https://doi.org/10.1080/08880018.2022.2047850

Dogs began playing new roles as emotional companions in Eurowestern households during the mid-1800s; by the 1880s, dogs were widely considered ‘family members’ in middle-class homes. The nineteenth-century ‘dog drama’, a type of melodrama, helps illuminate how, when, and why this attitudinal change occurred. The transatlantic appeal of dog dramas (such as René-Charles Guilbert de Pixérécourt's 1814 melodrama The Dog of Montargis) and performers who specialised in the genre (such as U.S. actor-entrepreneur Edwin Blanchard) suggest that sentimental stories about dogs appealed to working-class people as well. These plays reflected, and perhaps contributed to, changing views about dogs during the nineteenth century. The dog drama and its afterlives (in film, television, and social media) shed light on both the good intentions and troubling contradictions inherent in humans’ relationships with nonhuman animals, especially pets.

BACKGROUND Social connectedness decreases human mortality, improves cancer survival, cardiovascular health, and body mass, results in better-controlled glucose levels, and strengthens mental health. However, few public health studies have leveraged large social media data sets to classify user network structure and geographic reach rather than the sole use of social media platforms. OBJECTIVE The objective of this study was to determine the association between population-level digital social connectedness and reach and depression in the population across geographies of the United States. METHODS Our study used an ecological assessment of aggregated, cross-sectional population measures of social connectedness, and self-reported depression across all counties in the United States. This study included all 3142 counties in the contiguous United States. We used measures obtained between 2018 and 2020 for adult residents in the study area. The study’s main exposure of interest is the Social Connectedness Index (SCI), a pair-wise composite index describing the “strength of connectedness between 2 geographic areas as represented by Facebook friendship ties.” This measure describes the density and geographical reach of average county residents’ social network using Facebook friendships and can differentiate between local and long-distance Facebook connections. The study’s outcome of interest is self-reported depressive disorder as published by the Centers for Disease Control and Prevention. RESULTS On average, 21% (21/100) of all adult residents in the United States reported a depressive disorder. Depression frequency was the lowest for counties in the Northeast (18.6%) and was highest for southern counties (22.4%). Social networks in northeastern counties involved moderately local connections (SCI 5-10 the 20th percentile for n=70, 36% of counties), whereas social networks in Midwest, southern, and western counties contained mostly local connections (SCI 1-2 the 20th percentile for n=598, 56.7%, n=401, 28.2%, and n=159, 38.4%, respectively). As the quantity and distance that social connections span (ie, SCI) increased, the prevalence of depressive disorders decreased by 0.3% (SE 0.1%) per rank. CONCLUSIONS Social connectedness and depression showed, after adjusting for confounding factors such as income, education, cohabitation, natural resources, employment categories, accessibility, and urbanicity, that a greater social connectedness score is associated with a decreased prevalence of depression. CLINICALTRIAL

Obesity is associated with kidney stone disease, but it is unknown whether this association differs by SES. This study assessed the extent to which obesity and neighborhood characteristics jointly contribute to urinary risk factors for kidney stone disease.This was a retrospective analysis of adult patients with kidney stone disease evaluated with 24-hour urine collection (2001-2020). Neighborhood-level socioeconomic data were obtained for a principal component analysis, which identified 3 linearly independent factors. Associations between these factors and 24-hour urine measurements were assessed using linear regression as well as groupings of 24-hour urine results using multivariable logistic regression. Finally, multiplicative interactions were assessed testing effect modification by obesity, and analyses stratified by obesity were performed. Analyses were performed in 2021.In total, 1,264 patients met the study criteria. Factors retained on principal component analysis represented SES, family structure, and housing characteristics. On linear regression, there was a significant inverse correlation between SES and 24-hour urine sodium (p=0.0002). On multivariable logistic regression, obesity was associated with increased odds of multiple stone risk factors (OR=1.61; 95% CI=1.15, 2.26) and multiple dietary factors (OR=1.33; 95% CI=1.06, 1.67). No significant and consistent multiplicative interactions were observed between obesity and quartiles of neighborhood SES, family structure, or housing characteristics.Obesity was associated with the presence of multiple stone risk factors and multiple dietary factors; however, the strength and magnitude of these associations did not vary significantly by neighborhood SES, family structure, and housing characteristics.

Background: Informal learning experiences in science, technology, engineering, and math (STEM) can enhance STEM learning that occurs in formal educational settings and curricula as well as generate enthusiasm for considering STEM careers. The aim of this systematic review is to focus on the experiences of neurodiverse students in informal STEM learning. Neurodiversity is a subgroup of neurodevelopmental conditions, such as autism, attention deficit disorder, dyslexia, dyspraxia, and other neurological conditions. The neurodiversity movement regards these conditions as natural forms of human variation, as opposed to dysfunction, and recognizes that neurodiverse individuals possess many strengths relevant to STEM fields. Methods: The authors will systematically search electronic databases for relevant research and evaluation articles addressing informal STEM learning for K-12 children and youth with neurodiverse conditions. Eight databases and content-relevant websites (e.g., informalscience.org) will be searched using a predetermined search strategy and retrieved articles will be screened by two members of the research team. Data synthesis will include meta-analysis and meta-synthesis techniques, depending on the quantitative, qualitative, or mixed methods designs of the studies. Discussion: The synthesis of the findings resulting from various research and evaluation designs, across the K-12 age span, and across various informal STEM learning contexts, will lead to depth and breadth of understanding of ways to improve informal STEM learning programs for neurodiverse children and youth. The identification of informal STEM learning program components and contexts shown to yield positive results will provide specific recommendations for improving inclusiveness, accessibility, and STEM learning for neurodiverse children and youth. Systematic Review Registration: The current study has been registered in PROSPERO. Registration number: CRD42021278618

Background We explore the ivermectin discourse and sentiment in the United States with a special focus on political leaning through the social media blogging site Twitter. Methods We used sentiment analysis and topic modeling to geospatially explore ivermectin Twitter discourse in the United States and compared it to the political leaning of a state based on the 2020 presidential election. Results All modeled topics were associated with a negative sentiment. Tweets originating from democratic leaning states were more likely to be negative. Conclusions Real-time analysis of social media content can identify public health concerns and guide timely public health interventions tackling disinformation.

The COVID-19 pandemic has challenged the United States’ existing national public health informatics infrastructure. This report details the factors that have contributed to COVID-19 data inaccuracies and reporting delays and their effect on the modeling and monitoring of the COVID-19 pandemic.

Background:Incidence rates of gastric cancer are increasing in young adults (age Methods:We used population-based data from the California and Texas Cancer Registries from 1995 to 2016 to estimate age-adjusted incidence rates of EOGC among Hispanic and non-Hispanic White persons by year, sex, tumor stage, census tract poverty level, metro versus nonmetro county, and state. We used logistic regression models to identify factors associated with distant stage diagnosis.Results:Of 3,047 persons diagnosed with EOGC, 73.2% were Hispanic White. Incidence rates were 1.29 [95% confidence interval (CI), 1.24–1.35] and 0.31 (95% CI, 0.29–0.33) per 100,000 Hispanic White and non-Hispanic White persons, respectively, with consistently higher incidence rates among Hispanic persons at all levels of poverty. There were no statistically significant associations between ethnicity and distant stage diagnosis in adjusted analysis.Conclusions:There are ethnic disparities in EOGC incidence rates that persist across poverty levels.Impact:EOGC incidence rates vary by ethnicity and poverty; these factors should be considered when assessing disease risk and targeting prevention efforts.

Background Among Latinas with breast cancer, residence in an ethnic enclave may be associated with survival. However, findings from prior studies are inconsistent. Methods The authors conducted parallel analyses of California and Texas cancer registry data for adult (aged ≥18 years) Latinas who were diagnosed with invasive breast cancer from 1996 to 2005, with follow-up through 2014. Existing indices applied to tract-level 2000 US Census data were used to measure Latinx enclaves and neighborhood socioeconomic status (nSES). Multivariable Cox proportional hazard models were fit for all-cause and breast cancer-specific survival adjusted for year of diagnosis, patient age, nativity (with multiple imputation), tumor stage, histology, grade, size, and clustering by census tract. Results Among 38,858 Latinas, the majority (61.3% in California and 70.5% in Texas) lived in enclaves. In fully adjusted models for both states, foreign-born women were found to be more likely to die of breast cancer and all causes when compared with US-born women. Living in enclaves and in neighborhoods with higher SES were found to be independently associated with improved survival from both causes. When combined into a 4-level variable, those in low nSES nonenclaves had worse survival for both causes compared with those living in low nSES enclaves and, in the all-cause but not breast cancer-specific models, those in high nSES neighborhoods, regardless of enclave status, had improved survival from all causes. Conclusions Applying the same methods across 2 states eliminated previously published inconsistent associations between enclave residence and breast cancer survival. Future studies should identify specific protective effects of enclave residence to inform interventions.

Purpose Latino day laborers are male immigrants from mainly Mexico and Central America who congregate at corners, that is, informal hiring sites, to solicit short-term employment. Studies describing the occupational environment of Latino day laborers traditionally measure jobsite exposures, not corner exposures. We sought to elucidate exposures at corners by describing their demographic, socioeconomic, occupational, business, built, and physical environmental characteristics and by comparing corner characteristics with other locations in a large urban county in Texas. Methods We used multiple publicly available data sets from the U.S. Census, local tax authority, Google's Nearby Places Application Programming Interface, and Environmental Protection Agency at fine spatial scale to measure 34 characteristics of corners with matched comparison locations. Results Corners were located close to highways, high-traffic intersections, hardware and moving stores, and gas stations. Corners were in neighborhoods with large foreign-born and Latino populations, high rates of limited English proficiency, and high construction-sector employment. Conclusions Publicly available data sources describe demographic, socioeconomic, occupational, business, built, and physical environment characteristics of urban environments at fine spatial scale. Using these data, we identified unique corner-based exposures experienced by day laborers. Future research is needed to understand how corner environments may influence health for this uniquely vulnerable population.

BackgroundMonitoring disease incidence rates over time with population surveillance data is fundamental to public health research and practice. Bayesian disease monitoring methods provide advantages over conventional methods including greater flexibility in model specification and the ability to conduct formal inference on model-derived quantities of interest. However, software platforms for Bayesian inference are often inaccessible to nonspecialists.ObjectiveTo increase the accessibility of Bayesian methods among health surveillance researchers, we introduce a Bayesian methodology and open source software package, surveil, for time-series modeling of disease incidence and mortality. Given case count and population-at-risk data, the software enables health researchers to draw inferences about underlying risk and derivative quantities including age-standardized rates, annual and cumulative percent change, and measures of inequality.MethodsWe specify a Poisson likelihood for case counts and model trends in log-risk using the first-difference (random-walk) prior. Models in the surveil R package were built using the Stan modeling language. We demonstrate the methodology and software by analyzing age-standardized colorectal cancer (CRC) incidence rates by race and ethnicity for non-Latino Black (Black), non-Latino White (White), and Hispanic/Latino (of any race) adults aged 50-79 years in Texas’s 4 largest metropolitan statistical areas between 1999 and 2018.ResultsOur analysis revealed a cumulative decline of 31% (95% CI –37% to –25%) in CRC risk among Black adults, 17% (95% CI –23% to –11%) for Latino adults, and 35% (95% CI –38% to –31%) for White adults from 1999 to 2018. None of the 3 observed groups experienced significant incidence reduction in the final 4 years of the study (2015-2018). The Black-White rate difference (per 100,000) was 44 (95% CI 30-57) in 1999 and 35 (95% CI 28-43) in 2018. Cumulatively, the Black-White gap accounts for 3983 CRC cases (95% CI 3746-4219) or 31% (95% CI 29%-32%) of total CRC incidence among Black adults in this period.ConclusionsStalled progress on CRC prevention and excess CRC risk among Black residents warrant special attention as cancer prevention and control priorities in urban Texas. Our methodology and software can help the public and health agencies monitor health inequalities and evaluate progress toward disease prevention goals. Advantages of the methodology over current common practice include the following: (1) the absence of piecewise linearity constraints on the model space, and (2) formal inference can be undertaken on any model-derived quantities of interest using Bayesian methods.

Hispanic children with acute lymphoblastic leukemia (ALL) experience poorer overall survival (OS) than non-Hispanic White children, however, few studies have investigated the social determinants of this disparity. In Texas, many Hispanic individuals reside in ethnic enclaves—areas with high concentrations of immigrants, ethnic-specific businesses, and language isolation, which are often socioeconomically deprived. We determined whether enclave residence was associated with ALL survival, overall and among Hispanic children. We computed Hispanic enclave index scores for Texas census tracts, and classified children (N = 4083) as residing in enclaves if their residential tracts scored in the highest statewide quintile. We used Cox regression to evaluate the association between enclave residence and OS. Five-year OS was 78.6% for children in enclaves, and 77.8% for Hispanic children in enclaves, both significantly lower (p &lt, 0.05) than the 85.8% observed among children not in enclaves. Children in enclaves had increased risk of death (hazard ratio (HR) 1.20, 95% confidence interval (CI) 1.01–1.49) after adjustment for sex, age at diagnosis, year of diagnosis, metropolitan residence and neighborhood socioeconomic deprivation and after further adjustment for child race/ethnicity (HR 1.19, 95% CI 0.97–1.45). We observed increased risk of death when analyses were restricted to Hispanic children specifically (HR 1.30, 95% CI 1.03–1.65). Observations suggest that children with ALL residing in Hispanic enclaves experience inferior OS.

INTRODUCTION AND OBJECTIVE:Significant increases in kidney stone prevalence have been reported for Black and Hispanic individuals in the US, but the gender-specific contribution to this trend is un...

Introduction: CV mortality has declined over 4 decades in the U.S. However, whether declines have been uniformly experienced across U.S. counties, and predictors of CV mortality trajectory are not known. Methods: County-level mortality data from 1980-2014 was obtained from the National Center for Health Statistics. We used a ClustMix approach to identify 3 distinct county phenogroups based on mortality trajectory. Adjusted multinomial logistic regression models were constructed to evaluate the associations between county-level characteristics (demographic, social, and health status) and CV mortality trajectory-based phenogroups. Results: Among 3,133 counties, there were parallel declines in CV mortality in all groups (Fig.1A). High-mortality counties were located in the South and parts of the Ohio and Mississippi River valleys (Fig. 1B). County phenogroups varied significantly in social characteristics such as non-white proportion (low vs. high mortality: 12% vs. 27%), high-school education (11% vs. 20%), and violent crime rates (.01 vs. 0.3/100 population). Disparities in health factors were also observed with higher rates of smoking, obesity, and diabetes in the high (vs. low) mortality groups. A substantial collinearity was observed between social and health factors. In adjusted analysis, social, environmental, and health characteristics explained 56% variance in the county-level CV mortality trajectory. Education status (OR [95% CI]=12.4 [9.4-16.3]), violent crime rates (OR [95% CI] =1.6 [1.3-1.9]), and smoking (OR [95% CI] = 3.9 [3.1- 4.9]) were the strongest predictors of high mortality trajectory phenogroup membership (ref: low mortality). Conclusions: Despite a decline in CV mortality, disparities at the county-level have persisted over the past 4 decades largely driven by differences in social characteristics and smoking prevalence. This highlights the need for multi-domain interventions focusing on safety, education and public health to improve county-level disparities in CV health.

Background: Acute lymphoblastic leukemia (ALL) remains a leading cause of death in children and adolescents. We have demonstrated that Hispanic enclaves - neighborhoods with high proportions of Spanish-speaking residents, recent immigrants, and ethnic-specific businesses - are associated with inferior overall survival in children with ALL. However, associations of enclaves with other outcomes remain poorly understood. In the present study, our objective was to determine whether residence in an enclave was associated with end-induction minimal residual disease (MRD), a strong predictor of ALL mortality. Methods: This was a retrospective study of N=142 children aged 0-18 years, treated at Texas Children’s Hospital (Houston, TX) from 2007-2018. MRD was defined as ≥0.01% leukemic blasts in bone marrow at day 29 of therapy. We used the 2010 census tract geography and a modified version of the Hispanic enclave index to identify enclaves. We assigned an enclave index score to each census tract, computed quartiles based on the statewide distribution, and mapped children to quartiles based on address at diagnosis (Q1: least ethnically distinct neighborhoods; Q4: most ethnically distinct neighborhoods). Data on sex, self-reported race/ethnicity, National Cancer Institute (NCI) risk group, primary language, and ALL cytogenetics were abstracted from electronic health records. Cytogenetics were considered either unfavorable (BCR-ABL1 fusion, KMT2A rearrangement, hypodiploidy, or intrachromosomal amplification of chromosome 21) or favorable (ETV6-RUNX1 fusion, double trisomies of chromosomes 4 and 10)/neutral (neither favorable nor unfavorable). We estimated the odds ratio (OR) and 95% confidence interval (95% CI) of MRD according to enclave index score using logistic regression. Results: Neither individual ethnicity nor primary language varied by MRD status, but a greater proportion of MRD-positive children lived in the most ethnically distinct neighborhoods (53% vs. 36%, p=0.04). Enclave index score was associated with MRD after adjusting for sex, NCI risk group, and cytogenetics (OR 1.57 per quartile increment, 95% CI 1.09-2.33). We also observed non-significantly increased odds of MRD among children in Q3 (OR 1.53, 95% CI 0.41-6.55) and Q4 (OR 2.84, 0.83-11.58) relative to Q1. Conclusions: We found that residence in a Hispanic enclave was associated with MRD in children with ALL. Neither ethnicity nor primary language differed by MRD status, suggesting that these did not confound the association with enclave index score. Neighborhood factors may influence early treatment outcomes in ALL, and children living in Hispanic enclaves may constitute a high-risk population. Citation Format: Joshua P. Muniz, John P. Woodhouse, Amy E. Hughes, Sandi L. Pruitt, Karen R. Rabin, Michael E. Scheurer, Philip J. Lupo, Jeremy M. Schraw. Association between residence in a Hispanic enclave and end-induction minimal residual disease among children with acute lymphoblastic leukemia in Texas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3671.

PURPOSE: To review the available evidence regarding health disparities in kidney stone disease and identify knowledge gaps in this area. MATERIALS AND METHODS: A literature search was conducted using PubMed, Embase, and Scopus, limited to articles published in English, from 1971 to 2020. Articles were selected based on their relevance to disparities in kidney stone disease among adults in the United States. RESULTS: Several large epidemiologic studies suggest disproportionate increases in incidence and prevalence of kidney stone disease among women as well as Black and Hispanic individuals in the United States, whereas other studies of comparable size do not report racial and ethnic demographics. Numerous articles describe disparities in imaging utilization, metabolic workup completion, analgesia, surgical intervention, stone burden at presentation, surgical complications, follow-up, and quality of life based on race, ethnicity, socioeconomic status, and place of residence. Differences in urinary parameters based on race, ethnicity, and socioeconomic status may be explained by both dietary and physiologic factors. All articles assessed had substantial risk of selection bias and confounding. CONCLUSIONS: Health disparities are present in many aspects of kidney stone disease. Further research should focus not only on characterization of these disparities but also on interventions to reduce or eliminate them.

Hardworking actor, playwright, and stage manager Harry Watkins (1825–94) was also a prolific diarist. For fifteen years Watkins regularly recorded the plays he saw, the roles he performed, the books he read, and his impressions of current events. Performing across the U.S., Watkins collaborated with preeminent performers and producers, recording his successes and failures as well as his encounters with celebrities such as P. T. Barnum, Junius Brutus Booth, Edwin Forrest, Anna Cora Mowatt, and Lucy Stone. His is the only known diary of substantial length and scope written by a U.S. actor before the Civil War—making Watkins, essentially, the antebellum equivalent of Samuel Pepys. Theater historians Amy E. Hughes and Naomi J. Stubbs have selected, edited, and annotated excerpts from the diary in an edition that offers a vivid glimpse of how ordinary people like Watkins lived, loved, struggled, and triumphed during one of the most tumultuous periods in U.S. history. The selections in A Player and a Gentleman are drawn from a more expansive digital archive of the complete diary. The book, like its digital counterpart, will richly enhance our knowledge of antebellum theater culture and daily life in the U.S. during this period.

Introduction: Regional heterogeneity in cardiovascular disease (CVD) risk has been demonstrated in the United States (US), however associations between county-level social and demographic risk factors and CVD mortality have not previously been described. To address this, we evaluated whether unsupervised machine learning-based clustering could identify distinct US county subgroups with unique CVD outcomes. Methods: The study included 2,676 counties from the 2020 County Health Rankings program. Unsupervised hierarchical clustering of 46 candidate variables encompassing demographic, health behaviors, socioeconomic factors, and healthcare access domains was used to derive phenogroups. The association of phenogroups and age-adjusted CVD mortality was assessed by linear regression. Results: Clustering identified 4 phenogroups based on within-cluster inertia. Cluster 1 (N=924; 24.5%) counties were largely white, suburban households with high income and access to healthcare. Cluster 2 counties (N=451; 16.9%) were large with predominantly Hispanic residents and below average prevalence of CVD risk factors. Cluster 3 (N=951; 35.5%) counties included rural, white residents with the lowest levels of healthcare access. Cluster 4 (350; 13.1%) counties were comprised of predominantly black residents with substantial cardiovascular comorbidities and physical and socioeconomic burdens. Age-adjusted cardiovascular mortality increased in a stepwise fashion from 247 in cluster 1 to 349 per 100,000 residents in cluster 4 (FigA) with the phenogroups demonstrating regionality across the US (FigB). Addition of phenogroup in linear regression improved model performance with a R 2 of 0.70. Conclusions: Unsupervised machine learning clustering based on demographic and behavior data can identify unique county phenogroups with differential risk of CVD mortality and may aid in identifying communities at highest risk for CVD-related adverse events.

Introduction Significant heterogeneity in cardiovascular disease (CVD) risk and healthcare resource allocation has been demonstrated in the United States, but optimal methods to capture heterogeneity in county-level characteristics that contribute to CVD mortality differences are unclear. We evaluated the feasibility of unsupervised machine learning (ML)-based phenomapping in identifying subgroups of county-level social and demographic risk factors with differential CVD outcomes. Methods We performed a cross-sectional study using county-level data from 2008 to 2018 from the Centers for Disease Control (CDC) WONDER platform and the 2020 Robert Wood Johnson County Health Rankings program. Unsupervised clustering was performed on 46 facets of population characteristics spanning the demographic, health behaviors, socioeconomic, and healthcare access domains. Spatial autocorrelation was assessed using the Moran’s I test, and temporal trends in age-adjusted CVD outcomes were evaluated using linear mixed effect models and least square means. Results Among 2676 counties, 4 county-level phenogroups were identified (Moran’s I p-value, Highlights: (no more than 85 characters each with spaces) • Machine learning identified 4 county-level phenogroups with unique cardiovascular risk. • Phenogroups were driven primarily by social factors and access to health care. • Self-identified race/ethnicity was a driver of group membership in three of four phenogroups.

This paper proposes a Bayesian method for spatial regression using eigenvector spatial filtering (ESF) and Piironen and Vehtari's (2017) regularized horseshoe (RHS) prior. ESF models are most often estimated using variable selection procedures such as stepwise selection, but in the absence of a Bayesian model averaging procedure variable selection methods cannot properly account for parameter uncertainty. Hierarchical shrinkage priors such as the RHS address the foregoing concern in a computationally efficient manner by encoding prior information about spatial filters into an adaptive prior distribution which shrinks posterior estimates towards zero in the absence of a strong signal while only minimally regularizing coefficients of important eigenvectors. This paper presents results from a large simulation study which compares the performance of the proposed Bayesian model (RHS-ESF) to alternative spatial models under a variety of spatial autocorrelation scenarios. The RHS-ESF model performance matched that of the SAR model from which the data was generated. The study highlights that reliable uncertainty estimates require greater attention to spatial autocorrelation in covariates than is typically given. A demonstration analysis of 2016 U.S. Presidential election results further evidences robustness of results to hyper-prior specifications as well as the advantages of estimating spatial models using the Stan probabilistic programming language.

For US actor, playwright, and theatre manager Harry Watkins (1825–94), the 1852–3 season was a whirlwind of ups and downs, elation and despair, triumph and tragedy. His engagement as an actor in C. R. Thorne's stock company at the New York Theatre ended abruptly in mid-September, leaving him without work at a point when few theatres were hiring. He mourned the loss of a beloved cousin, Jane Mott, who passed away one rainy day in October after a drawn-out illness. He endured many a headache while spearheading a fund-raising effort among his fellow actors to contribute a memorial stone to the Washington Monument. He was elected to the board of the American Dramatic Fund Association, but infighting among the directors left him feeling insulted and underappreciated, ultimately leading him to cease his involvement. By far, his biggest frustration was his inability to obtain reliable employment. He wrote many letters to many managers, to no avail. More than once, he considered giving up the theatre altogether.

Objectives. To demonstrate the benefits-mapping software Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE), which integrates local air quality data with previously published concentration–response and health–economic valuation functions to estimate the health effects of changes in air pollution levels and their economic consequences. Methods. We illustrate a local health impact assessment of ozone changes in the 10-county nonattainment area of the Dallas–Fort Worth region of Texas, estimating the short-term effects on mortality predicted by 2 scenarios for 3 years (2008, 2011, and 2013): an incremental rollback of the daily 8-hour maximum ozone levels of all area monitors by 10 parts per billion and a rollback-to-a-standard ambient level of 65 parts per billion at only monitors above that level. Results. Estimates of preventable premature deaths attributable to ozone air pollution obtained by the incremental rollback method varied little by year, whereas those obtained by the rollback-to-a-standard method varied by year and were sensitive to the choice of ordinality and the use of preloaded or imported data. Conclusions. BenMAP-CE allows local and regional public health analysts to generate timely, evidence-based estimates of the health impacts and economic consequences of potential policy options in their communities.

Key Points Question How have trajectories of cardiovascular (CV) mortality among US counties varied over 35 years, and are county-level factors associated with this variation? Findings In this cross-sectional study, cardiovascular mortality declined in all groups from 1980 to 2014 in this cross-sectional analysis of 3133 US counties. Three unique phenogroups based on mortality trajectory were identified, with the difference between high- and low-mortality counties unchanged during the study period, and were associated with social, behavioral, and environmental characteristics at the county level. Meaning These findings suggest that despite an overall decline in CV mortality rates over the past 35 years, differences in health behavior patterns and other societal risk factors are associated with persistent CV mortality disparities at the county level., This cross-sectional study identifies clusters based on cardiovascular mortality trajectory among US counties and county-level social, demographic, environmental, and health-related risk factors that are associated with these clusters., Importance Cardiovascular (CV) mortality has declined for more than 3 decades in the US. However, differences in declines among residents at a US county level are not well characterized. Objective To identify unique county-level trajectories of CV mortality in the US during a 35-year study period and explore county-level factors that are associated with CV mortality trajectories. Design, Setting, and Participants This longitudinal cross-sectional analysis of CV mortality trends used data from 3133 US counties from 1980 to 2014. County-level demographic, socioeconomic, environmental, and health-related risk factors were compiled. Data were analyzed from December 2019 to September 2021. Exposures County-level characteristics, collected from 5 county-level data sets. Main Outcomes and Measures Cardiovascular mortality data were obtained for 3133 US counties from 1980 to 2014 using age-standardized county-level mortality rates from the Global Burden of Disease study. The longitudinal K-means approach was used to identify 3 distinct clusters based on underlying mortality trajectory. Multinomial logistic regression models were constructed to evaluate associations between county characteristics and cluster membership. Results Among 3133 US counties (median, 49.5% [IQR, 48.9%-50.5%] men; 30.7% [IQR, 27.1%-34.4%] older than 55 years; 9.9% [IQR, 4.5%-22.7%] racial minority group [individuals self-identifying as Black or African American, American Indian or Alaska Native, Asian, Native Hawaiian, Pacific Islander, other, or multiple races/ethnicities]), CV mortality declined by 45.5% overall and by 38.4% in high-mortality strata (694 counties), by 45.0% in intermediate-mortality strata (1382 counties), and by 48.3% in low-mortality strata (1057 counties). Counties with the highest mortality in 1980 continued to demonstrate the highest mortality in 2014. Trajectory groups were regionally distributed, with high-mortality trajectory counties focused in the South and in portions of Appalachia. Low- vs high-mortality groups varied significantly in demographic (racial minority group proportion, 7.6% [IQR, 4.1%-14.5%]) vs 23.9% [IQR, 6.5%-40.8%]) and socioeconomic characteristics such as high-school education (9.4% [IQR, 7.3%-12.6%] vs 20.1% [IQR, 16.1%-23.2%]), poverty rates (11.4% [IQR, 8.8%-14.6%] vs 20.6% [IQR, 17.1%-24.4%]), and violent crime rates (161.5 [IQR, 89.0-262.4] vs 272.8 [IQR, 155.3-431.3] per 100 000 population). In multinomial logistic regression, a model incorporating demographic, socioeconomic, environmental, and health characteristics accounted for 60% of the variance in the CV mortality trajectory (R2 = 0.60). Sociodemographic factors such as racial minority group proportion (odds ratio [OR], 1.70 [95% CI, 1.35-2.14]) and educational attainment (OR, 6.17 [95% CI, 4.55-8.36]) and health behaviors such as smoking (OR for high vs low, 2.04 [95% CI, 1.58-2.64]) and physical inactivity (OR, 3.74 [95% CI, 2.83-4.93]) were associated with the high-mortality trajectory. Conclusions and Relevance Cardiovascular mortality declined in all subgroups during the 35-year study period; however, disparities remained unchanged during that time. Disparate trajectories were associated with social and behavioral risks. Health policy efforts across multiple domains, including structural and public health targets, may be needed to reduce existing county-level cardiovascular mortality disparities.

Low–socioeconomic status (SES) households have little income or wealth to buffer against the negative impacts of adverse health events among adult household members. This research project links data from a nonprofit food distribution center, electronic medical records from a safety-net healthcare system, and publicly available residential appraisals for more than 3,000 households to provide insight into how low-SES households cope with health shocks experienced by resident adults. Three broad types of strategies are examined: changes in household structure, residential mobility, and use of social services. Of the households studied, 20.2 percent had at least one adult member who experienced a health shock. These households were more likely to gain additional adult household members and employed household members, were more likely to move residence and to move distances greater than one mile, and were less likely to visit the food distribution center after the shock. This research highlights how novel data linkages can help us to understand how health and social policies impact vulnerable populations.

Limited spatial accessibility to mammography, and socioeconomic barriers (e.g., being uninsured), may contribute to rural disparities in breast cancer screening. Although mobile mammography may contribute to population-level access, few studies have investigated this relationship. We measured mammography access for uninsured women using the variable two-step floating catchment area (V2SFCA) method, which estimates access at the local level using estimated potential supply and demand. Specifically, we measured supply with mammography machine certifications in 2014 from FDA and brick-and-mortar and mobile facility data from the community-based Breast Screening and Patient Navigation (BSPAN) program. We measured potential demand using Census tract-level estimates of female residents aged 45-74 from 5-year 2012-2016 American Community Survey data. Using the sign test, we compared mammography access estimates based on 3 facility groupings: FDA-certified, program brick-and-mortar only, and brick-and-mortar plus mobile. Using all mammography facilities, accessibility was high in urban Dallas-Ft. Worth, low for the ring of adjacent counties, and high for rural counties outlying this ring. Brick-and-mortar-based estimates were lower for the outlying ring, and mobile-unit contribution to access was observed more in urban tracts. Weak mobile-unit contribution across the study area may indicate suboptimal dispatch of mobile units to locations. Geospatial methods could identify the optimal locations for mobile units, given existing brick-and-mortar facilities, to increase access for underserved areas.

Introduction: Many Latinos live in ethnic enclaves—culturally distinct neighborhoods with high concentrations of individuals of the same ethnic origin, high linguistic isolation, a large share of recent immigrants, and ethnic specific businesses and resources. Prior studies on the association of enclave residence and cancer mortality were often limited to a single state and demonstrated mixed results. It is unclear whether the mixed results are due to widely varying measures and analytic methods, or true regional differences in enclave effects. Methods: We conducted parallel analyses of California and Texas cancer registry data from adult (≥18 years of age) Latinas diagnosed with invasive breast cancer from 1996 to 2005, with follow-up through 2014. Our key focus was to use the same measures and methods. We linked 2000 U.S. Census data to measure Latino enclaves and neighborhood socioeconomic status (nSES). We defined enclaves using an established multidimensional index of seven census tract measures (percent of residents who are Latino, foreign-born, recent immigrants, and linguistically isolated (general and of those who speak Spanish), with limited English proficiency (general and of those who speak Spanish) split into statewide quintiles. We fitted Cox proportional hazard models for all-cause and breast cancer specific mortality adjusted for year of diagnosis, patient age, birthplace (with multiple imputation), tumor stage, histology, grade, and size, and clustering by census tract. We explored interactions of enclave residence with nSES and patient birthplace. Results: Among 40,716 Latinas, the majority (61.3% in CA, 70.5% in TX) lived in ethnic enclaves. Ethnic enclave residence was more common among foreign- vs U.S.-born Latinas (72.0% vs 52.3% in CA and 82.4% vs. 68.5% in TX). In fully adjusted models for both states, foreign- vs. US-born women were more likely to die from breast cancer and all causes. Living in an ethnic enclave and in neighborhoods with higher SES was independently associated with decreased mortality. Patterns were consistent in terms of direction and significance of associations across states for all-cause and breast cancer-specific mortality. Future analyses will further explore the statistically significant interactions (p Discussion: Applying the same methods across two states eliminated previously published inconsistent results about the association of enclave residence and mortality among Latinas with breast cancer. Future studies should consider pooling cancer registry data across states and using the same methods to better understand the true impact of ethnic enclave residence across the cancer continuum. Future studies should also focus on identifying the specific protective effects of enclave residence to inform interventions. This abstract is also being presented as Poster C073. Citation Format: Salma Shariff-Marco, Scarlett Lin Gomez, Alison Canchola, Hannah Fullington, Amy E. Hughes, Sandi L. Pruitt. Do Latinas with breast cancer who live in ethnic enclaves have better or worse survival? Analysis of cancer registry data from California and Texas [abstract]. In: Proceedings of the Eleventh AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2018 Nov 2-5; New Orleans, LA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl):Abstract nr PR14.

Screening with FIT or colonoscopy can reduce CRC mortality. In our pragmatic, randomized trial of screening outreach over three years, patients annually received mailed FITs or colonoscopy invitations. We examined screening initiation after each mailing and crossover from the invited to other modality. Eligible patients (50–64 years, ≥1 primary-care visit before randomization, and no history of CRC) received mailed FIT kits (n = 2400) or colonoscopy invitations (n = 2400) from March 2013 through July 2016. Among those invited for colonoscopy, we used multinomial logistic regression to identify factors associated with screening initiation with colonoscopy vs. FIT vs. no screening after the first mailing. Most patients were female (61.8%) and Hispanic (48.9%) or non-Hispanic black (24.0%). Among those invited for FIT, 56.6% (n = 1359) initiated with FIT, whereas 3.3% (n = 78) crossed over to colonoscopy; 151 (15.7%) and 61 (7.7%) initiated with FIT after second and third mailings. Among those invited for colonoscopy, 25.5% (n = 613) initiated with colonoscopy whereas 18.8% (n = 452) crossed over to FIT; 112 (8.4%) and 48 (4.2%) initiated with colonoscopy after second and third mailings. Three or more primary-care visits prior to randomization were associated with initiating with colonoscopy (OR 1.49, 95% CI 1.17–1.91) and crossing over to FIT (OR 1.63, 95% CI 1.19–2.23). Although nearly half of patients initiated screening after the first mailing, few non-responders in either outreach group initiated after a second or third mailing. More patients invited to colonoscopy crossed over to FIT than those assigned to FIT crossed over to colonoscopy.